pwd

'C:\\Users\\VIPUL'

cd G:\ANTWALK\Python Project\Python Project Solution
    pwd

  File "C:\Users\VIPUL\AppData\Local\Temp/ipykernel_3768/2583361278.py", line 1
    cd G:\ANTWALK\Python Project\Python Project Solution
       ^
SyntaxError: invalid syntax

cd G:\ANTWALK\Python Project\Python Project Solution

G:\ANTWALK\Python Project\Python Project Solution

pwd

'G:\\ANTWALK\\Python Project\\Python Project Solution'

import pandas as pd
df =pd.read_csv('G:\ANTWALK\Python Project\Python Project Solution\Given_Dataset\Customer Attrition Status.csv')
df

	RowNumber	CustomerId	Exited
0	1	15634602.0	1.0
1	2	15647311.0	0.0
2	3	15619304.0	1.0
3	4	15701354.0	0.0
4	5	15737888.0	0.0
...	...	...	...
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

10019 rows × 3 columns

df.shape

(10019, 3)

df.describe

df.describe

<bound method NDFrame.describe of        RowNumber  CustomerId  Exited
0              1  15634602.0     1.0
1              2  15647311.0     0.0
2              3  15619304.0     1.0
3              4  15701354.0     0.0
4              5  15737888.0     0.0
...          ...         ...     ...
10014      10015         NaN     NaN
10015      10016         NaN     NaN
10016      10017         NaN     NaN
10017      10018         NaN     NaN
10018      10019         NaN     NaN

[10019 rows x 3 columns]>

df.head(7)

	RowNumber	CustomerId	Exited
0	1	15634602.0	1.0
1	2	15647311.0	0.0
2	3	15619304.0	1.0
3	4	15701354.0	0.0
4	5	15737888.0	0.0
5	6	15574012.0	1.0
6	7	15592531.0	0.0

df.tail(7)

	RowNumber	CustomerId	Exited
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df.isnull().sum()

RowNumber      0
CustomerId    19
Exited        19
dtype: int64

df

	RowNumber	CustomerId	Exited
0	1	15634602.0	1.0
1	2	15647311.0	0.0
2	3	15619304.0	1.0
3	4	15701354.0	0.0
4	5	15737888.0	0.0
...	...	...	...
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

10019 rows × 3 columns

df2 = df[df.isnull().any(axis=1)]
df2

	RowNumber	CustomerId	Exited
10000	10001	NaN	NaN
10001	10002	NaN	NaN
10002	10003	NaN	NaN
10003	10004	NaN	NaN
10004	10005	NaN	NaN
10005	10006	NaN	NaN
10006	10007	NaN	NaN
10007	10008	NaN	NaN
10008	10009	NaN	NaN
10009	10010	NaN	NaN
10010	10011	NaN	NaN
10011	10012	NaN	NaN
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df2 = df[df.isnull().any(axis=1)]
df2

	RowNumber	CustomerId	Exited
10000	10001	NaN	NaN
10001	10002	NaN	NaN
10002	10003	NaN	NaN
10003	10004	NaN	NaN
10004	10005	NaN	NaN
10005	10006	NaN	NaN
10006	10007	NaN	NaN
10007	10008	NaN	NaN
10008	10009	NaN	NaN
10009	10010	NaN	NaN
10010	10011	NaN	NaN
10011	10012	NaN	NaN
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df2.dropna(inplace= True)

C:\ProgramData\Anaconda3\lib\site-packages\pandas\util\_decorators.py:311: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return func(*args, **kwargs)

df2 = df[df.isnull().any(axis=1)].copy((deep=True)
df2

  File "C:\Users\VIPUL\AppData\Local\Temp/ipykernel_3768/4034753541.py", line 1
    df2 = df[df.isnull().any(axis=1)].copy((deep=True)
                                                ^
SyntaxError: invalid syntax

import warnings
warnings.filterwarnings('ignore')

df.isnull().sum()

RowNumber      0
CustomerId    19
Exited        19
dtype: int64

df2 = df[df.isnull().any(axis=1)]
df2

	RowNumber	CustomerId	Exited
10000	10001	NaN	NaN
10001	10002	NaN	NaN
10002	10003	NaN	NaN
10003	10004	NaN	NaN
10004	10005	NaN	NaN
10005	10006	NaN	NaN
10006	10007	NaN	NaN
10007	10008	NaN	NaN
10008	10009	NaN	NaN
10009	10010	NaN	NaN
10010	10011	NaN	NaN
10011	10012	NaN	NaN
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df2.dropna(inplace= True)

df.isnull().sum()

RowNumber      0
CustomerId    19
Exited        19
dtype: int64

df2 = df[df.isnull().any(axis=1)]
df2

	RowNumber	CustomerId	Exited
10000	10001	NaN	NaN
10001	10002	NaN	NaN
10002	10003	NaN	NaN
10003	10004	NaN	NaN
10004	10005	NaN	NaN
10005	10006	NaN	NaN
10006	10007	NaN	NaN
10007	10008	NaN	NaN
10008	10009	NaN	NaN
10009	10010	NaN	NaN
10010	10011	NaN	NaN
10011	10012	NaN	NaN
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df2.dropna(inplace= True)

df2 = df[df.isnull().any(axis=1)]
df2

	RowNumber	CustomerId	Exited
10000	10001	NaN	NaN
10001	10002	NaN	NaN
10002	10003	NaN	NaN
10003	10004	NaN	NaN
10004	10005	NaN	NaN
10005	10006	NaN	NaN
10006	10007	NaN	NaN
10007	10008	NaN	NaN
10008	10009	NaN	NaN
10009	10010	NaN	NaN
10010	10011	NaN	NaN
10011	10012	NaN	NaN
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df3 = df[df.isnull().any(axis=1)]
df3

	RowNumber	CustomerId	Exited
10000	10001	NaN	NaN
10001	10002	NaN	NaN
10002	10003	NaN	NaN
10003	10004	NaN	NaN
10004	10005	NaN	NaN
10005	10006	NaN	NaN
10006	10007	NaN	NaN
10007	10008	NaN	NaN
10008	10009	NaN	NaN
10009	10010	NaN	NaN
10010	10011	NaN	NaN
10011	10012	NaN	NaN
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df.tail(20)

	RowNumber	CustomerId	Exited
9999	10000	15628319.0	0.0
10000	10001	NaN	NaN
10001	10002	NaN	NaN
10002	10003	NaN	NaN
10003	10004	NaN	NaN
10004	10005	NaN	NaN
10005	10006	NaN	NaN
10006	10007	NaN	NaN
10007	10008	NaN	NaN
10008	10009	NaN	NaN
10009	10010	NaN	NaN
10010	10011	NaN	NaN
10011	10012	NaN	NaN
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df2.dropna
df2

	RowNumber	CustomerId	Exited
10000	10001	NaN	NaN
10001	10002	NaN	NaN
10002	10003	NaN	NaN
10003	10004	NaN	NaN
10004	10005	NaN	NaN
10005	10006	NaN	NaN
10006	10007	NaN	NaN
10007	10008	NaN	NaN
10008	10009	NaN	NaN
10009	10010	NaN	NaN
10010	10011	NaN	NaN
10011	10012	NaN	NaN
10012	10013	NaN	NaN
10013	10014	NaN	NaN
10014	10015	NaN	NaN
10015	10016	NaN	NaN
10016	10017	NaN	NaN
10017	10018	NaN	NaN
10018	10019	NaN	NaN

df = df.dropna(axis=0)
print(df)

      RowNumber  CustomerId  Exited
0             1  15634602.0     1.0
1             2  15647311.0     0.0
2             3  15619304.0     1.0
3             4  15701354.0     0.0
4             5  15737888.0     0.0
...         ...         ...     ...
9995       9996  15606229.0     0.0
9996       9997  15569892.0     0.0
9997       9998  15584532.0     1.0
9998       9999  15682355.0     1.0
9999      10000  15628319.0     0.0

[10000 rows x 3 columns]

df.describe

<bound method NDFrame.describe of       RowNumber  CustomerId  Exited
0             1  15634602.0     1.0
1             2  15647311.0     0.0
2             3  15619304.0     1.0
3             4  15701354.0     0.0
4             5  15737888.0     0.0
...         ...         ...     ...
9995       9996  15606229.0     0.0
9996       9997  15569892.0     0.0
9997       9998  15584532.0     1.0
9998       9999  15682355.0     1.0
9999      10000  15628319.0     0.0

[10000 rows x 3 columns]>

df.tail(20)

	RowNumber	CustomerId	Exited
9980	9981	15719276.0	0.0
9981	9982	15672754.0	1.0
9982	9983	15768163.0	1.0
9983	9984	15656710.0	0.0
9984	9985	15696175.0	0.0
9985	9986	15586914.0	0.0
9986	9987	15581736.0	0.0
9987	9988	15588839.0	0.0
9988	9989	15589329.0	0.0
9989	9990	15605622.0	0.0
9990	9991	15798964.0	0.0
9991	9992	15769959.0	1.0
9992	9993	15657105.0	0.0
9993	9994	15569266.0	0.0
9994	9995	15719294.0	0.0
9995	9996	15606229.0	0.0
9996	9997	15569892.0	0.0
9997	9998	15584532.0	1.0
9998	9999	15682355.0	1.0
9999	10000	15628319.0	0.0

df.isnull().sum()

RowNumber     0
CustomerId    0
Exited        0
dtype: int64

df.head(6)

	RowNumber	CustomerId	Exited
0	1	15634602.0	1.0
1	2	15647311.0	0.0
2	3	15619304.0	1.0
3	4	15701354.0	0.0
4	5	15737888.0	0.0
5	6	15574012.0	1.0

df

	RowNumber	CustomerId	Exited
0	1	15634602.0	1.0
1	2	15647311.0	0.0
2	3	15619304.0	1.0
3	4	15701354.0	0.0
4	5	15737888.0	0.0
...	...	...	...
9995	9996	15606229.0	0.0
9996	9997	15569892.0	0.0
9997	9998	15584532.0	1.0
9998	9999	15682355.0	1.0
9999	10000	15628319.0	0.0

10000 rows × 3 columns

dfdemog =pd.read_csv('G:\ANTWALK\Python Project\Python Project Solution\Given_Dataset\Customer Demographics.csv')
dfdemog

	RowNumber	CustomerId	Surname	Geography	Gender	Age
0	1	15634602.0	Hargrave	West	Female	42.0
1	2	15647311.0	Hill	Central	Female	41.0
2	3	15619304.0	Onio	West	Female	42.0
3	4	15701354.0	Boni	West	Female	39.0
4	5	15737888.0	Mitchell	Central	Female	43.0
...	...	...	...	...	...	...
10014	10015	NaN	NaN	NaN	NaN	NaN
10015	10016	NaN	NaN	NaN	NaN	NaN
10016	10017	NaN	NaN	NaN	NaN	NaN
10017	10018	NaN	NaN	NaN	NaN	NaN
10018	10019	NaN	NaN	NaN	NaN	NaN

10019 rows × 6 columns

dfdemog.tail(20)

	RowNumber	CustomerId	Surname	Geography	Gender	Age
9999	10000	15628319.0	Walker	West	Female	28.0
10000	10001	NaN	NaN	NaN	NaN	NaN
10001	10002	NaN	NaN	NaN	NaN	NaN
10002	10003	NaN	NaN	NaN	NaN	NaN
10003	10004	NaN	NaN	NaN	NaN	NaN
10004	10005	NaN	NaN	NaN	NaN	NaN
10005	10006	NaN	NaN	NaN	NaN	NaN
10006	10007	NaN	NaN	NaN	NaN	NaN
10007	10008	NaN	NaN	NaN	NaN	NaN
10008	10009	NaN	NaN	NaN	NaN	NaN
10009	10010	NaN	NaN	NaN	NaN	NaN
10010	10011	NaN	NaN	NaN	NaN	NaN
10011	10012	NaN	NaN	NaN	NaN	NaN
10012	10013	NaN	NaN	NaN	NaN	NaN
10013	10014	NaN	NaN	NaN	NaN	NaN
10014	10015	NaN	NaN	NaN	NaN	NaN
10015	10016	NaN	NaN	NaN	NaN	NaN
10016	10017	NaN	NaN	NaN	NaN	NaN
10017	10018	NaN	NaN	NaN	NaN	NaN
10018	10019	NaN	NaN	NaN	NaN	NaN

dfdemog.describe

<bound method NDFrame.describe of        RowNumber  CustomerId   Surname Geography  Gender   Age
0              1  15634602.0  Hargrave      West  Female  42.0
1              2  15647311.0      Hill   Central  Female  41.0
2              3  15619304.0      Onio      West  Female  42.0
3              4  15701354.0      Boni      West  Female  39.0
4              5  15737888.0  Mitchell   Central  Female  43.0
...          ...         ...       ...       ...     ...   ...
10014      10015         NaN       NaN       NaN     NaN   NaN
10015      10016         NaN       NaN       NaN     NaN   NaN
10016      10017         NaN       NaN       NaN     NaN   NaN
10017      10018         NaN       NaN       NaN     NaN   NaN
10018      10019         NaN       NaN       NaN     NaN   NaN

[10019 rows x 6 columns]>

dfdemog.isnull().sum()

RowNumber      0
CustomerId    19
Surname       19
Geography     19
Gender        19
Age           19
dtype: int64

dfdemog.dropna

<bound method DataFrame.dropna of        RowNumber  CustomerId   Surname Geography  Gender   Age
0              1  15634602.0  Hargrave      West  Female  42.0
1              2  15647311.0      Hill   Central  Female  41.0
2              3  15619304.0      Onio      West  Female  42.0
3              4  15701354.0      Boni      West  Female  39.0
4              5  15737888.0  Mitchell   Central  Female  43.0
...          ...         ...       ...       ...     ...   ...
10014      10015         NaN       NaN       NaN     NaN   NaN
10015      10016         NaN       NaN       NaN     NaN   NaN
10016      10017         NaN       NaN       NaN     NaN   NaN
10017      10018         NaN       NaN       NaN     NaN   NaN
10018      10019         NaN       NaN       NaN     NaN   NaN

[10019 rows x 6 columns]>

dfdemog.isnull().sum()

RowNumber      0
CustomerId    19
Surname       19
Geography     19
Gender        19
Age           19
dtype: int64

df3 = dfdemog.dropna(axis=0)
print(df3)

      RowNumber  CustomerId    Surname Geography  Gender   Age
0             1  15634602.0   Hargrave      West  Female  42.0
1             2  15647311.0       Hill   Central  Female  41.0
2             3  15619304.0       Onio      West  Female  42.0
3             4  15701354.0       Boni      West  Female  39.0
4             5  15737888.0   Mitchell   Central  Female  43.0
...         ...         ...        ...       ...     ...   ...
9995       9996  15606229.0   Obijiaku      West    Male  39.0
9996       9997  15569892.0  Johnstone      West    Male  35.0
9997       9998  15584532.0        Liu      West  Female  36.0
9998       9999  15682355.0  Sabbatini      East    Male  42.0
9999      10000  15628319.0     Walker      West  Female  28.0

[10000 rows x 6 columns]

dfdemog

	RowNumber	CustomerId	Surname	Geography	Gender	Age
0	1	15634602.0	Hargrave	West	Female	42.0
1	2	15647311.0	Hill	Central	Female	41.0
2	3	15619304.0	Onio	West	Female	42.0
3	4	15701354.0	Boni	West	Female	39.0
4	5	15737888.0	Mitchell	Central	Female	43.0
...	...	...	...	...	...	...
10014	10015	NaN	NaN	NaN	NaN	NaN
10015	10016	NaN	NaN	NaN	NaN	NaN
10016	10017	NaN	NaN	NaN	NaN	NaN
10017	10018	NaN	NaN	NaN	NaN	NaN
10018	10019	NaN	NaN	NaN	NaN	NaN

10019 rows × 6 columns

dfdemog = df3

df3 =pd.merge(df,dfdemog,on='CustomerId')
df3

	RowNumber_x	CustomerId	Exited	RowNumber_y	Surname	Geography	Gender	Age
0	1	15634602.0	1.0	1	Hargrave	West	Female	42.0
1	2	15647311.0	0.0	2	Hill	Central	Female	41.0
2	3	15619304.0	1.0	3	Onio	West	Female	42.0
3	4	15701354.0	0.0	4	Boni	West	Female	39.0
4	5	15737888.0	0.0	5	Mitchell	Central	Female	43.0
...	...	...	...	...	...	...	...	...
9995	9996	15606229.0	0.0	9996	Obijiaku	West	Male	39.0
9996	9997	15569892.0	0.0	9997	Johnstone	West	Male	35.0
9997	9998	15584532.0	1.0	9998	Liu	West	Female	36.0
9998	9999	15682355.0	1.0	9999	Sabbatini	East	Male	42.0
9999	10000	15628319.0	0.0	10000	Walker	West	Female	28.0

10000 rows × 8 columns

df

	RowNumber	CustomerId	Exited
0	1	15634602.0	1.0
1	2	15647311.0	0.0
2	3	15619304.0	1.0
3	4	15701354.0	0.0
4	5	15737888.0	0.0
...	...	...	...
9995	9996	15606229.0	0.0
9996	9997	15569892.0	0.0
9997	9998	15584532.0	1.0
9998	9999	15682355.0	1.0
9999	10000	15628319.0	0.0

10000 rows × 3 columns

df3

	RowNumber_x	CustomerId	Exited	RowNumber_y	Surname	Geography	Gender	Age
0	1	15634602.0	1.0	1	Hargrave	West	Female	42.0
1	2	15647311.0	0.0	2	Hill	Central	Female	41.0
2	3	15619304.0	1.0	3	Onio	West	Female	42.0
3	4	15701354.0	0.0	4	Boni	West	Female	39.0
4	5	15737888.0	0.0	5	Mitchell	Central	Female	43.0
...	...	...	...	...	...	...	...	...
9995	9996	15606229.0	0.0	9996	Obijiaku	West	Male	39.0
9996	9997	15569892.0	0.0	9997	Johnstone	West	Male	35.0
9997	9998	15584532.0	1.0	9998	Liu	West	Female	36.0
9998	9999	15682355.0	1.0	9999	Sabbatini	East	Male	42.0
9999	10000	15628319.0	0.0	10000	Walker	West	Female	28.0

10000 rows × 8 columns

df3 =pd.merge(df,dfdemog,on='CustomerId',how='left')
df3

	RowNumber_x	CustomerId	Exited	RowNumber_y	Surname	Geography	Gender	Age
0	1	15634602.0	1.0	1	Hargrave	West	Female	42.0
1	2	15647311.0	0.0	2	Hill	Central	Female	41.0
2	3	15619304.0	1.0	3	Onio	West	Female	42.0
3	4	15701354.0	0.0	4	Boni	West	Female	39.0
4	5	15737888.0	0.0	5	Mitchell	Central	Female	43.0
...	...	...	...	...	...	...	...	...
9995	9996	15606229.0	0.0	9996	Obijiaku	West	Male	39.0
9996	9997	15569892.0	0.0	9997	Johnstone	West	Male	35.0
9997	9998	15584532.0	1.0	9998	Liu	West	Female	36.0
9998	9999	15682355.0	1.0	9999	Sabbatini	East	Male	42.0
9999	10000	15628319.0	0.0	10000	Walker	West	Female	28.0

10000 rows × 8 columns

df4= df3.drop(['RowNumber_y'],axis = 1)

df4

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age
0	1	15634602.0	1.0	Hargrave	West	Female	42.0
1	2	15647311.0	0.0	Hill	Central	Female	41.0
2	3	15619304.0	1.0	Onio	West	Female	42.0
3	4	15701354.0	0.0	Boni	West	Female	39.0
4	5	15737888.0	0.0	Mitchell	Central	Female	43.0
...	...	...	...	...	...	...	...
9995	9996	15606229.0	0.0	Obijiaku	West	Male	39.0
9996	9997	15569892.0	0.0	Johnstone	West	Male	35.0
9997	9998	15584532.0	1.0	Liu	West	Female	36.0
9998	9999	15682355.0	1.0	Sabbatini	East	Male	42.0
9999	10000	15628319.0	0.0	Walker	West	Female	28.0

10000 rows × 7 columns

dfInv1 =pd.read_csv('G:\ANTWALK\Python Project\Python Project Solution\Given_Dataset\Customer Investment Snapshot.csv')

dfInv1

	RowNumber	CustomerId	Tenure	CreditScore	Balance	EstimatedSalary
0	1	15634602.0	2.0	619.0	NaN	101348.88
1	2	15647311.0	1.0	608.0	83807.86	112542.58
2	3	15619304.0	8.0	502.0	159660.80	113931.57
3	4	15701354.0	1.0	699.0	NaN	93826.63
4	5	15737888.0	2.0	850.0	125510.82	79084.10
...	...	...	...	...	...	...
10014	10015	NaN	NaN	NaN	NaN	NaN
10015	10016	NaN	NaN	NaN	NaN	NaN
10016	10017	NaN	NaN	NaN	NaN	NaN
10017	10018	NaN	NaN	NaN	NaN	NaN
10018	10019	NaN	NaN	NaN	NaN	NaN

10019 rows × 6 columns

dfInv1.describe

<bound method NDFrame.describe of        RowNumber  CustomerId  Tenure  CreditScore    Balance  EstimatedSalary
0              1  15634602.0     2.0        619.0        NaN        101348.88
1              2  15647311.0     1.0        608.0   83807.86        112542.58
2              3  15619304.0     8.0        502.0  159660.80        113931.57
3              4  15701354.0     1.0        699.0        NaN         93826.63
4              5  15737888.0     2.0        850.0  125510.82         79084.10
...          ...         ...     ...          ...        ...              ...
10014      10015         NaN     NaN          NaN        NaN              NaN
10015      10016         NaN     NaN          NaN        NaN              NaN
10016      10017         NaN     NaN          NaN        NaN              NaN
10017      10018         NaN     NaN          NaN        NaN              NaN
10018      10019         NaN     NaN          NaN        NaN              NaN

[10019 rows x 6 columns]>

dfInv1.isnull().sum()

RowNumber             0
CustomerId           19
Tenure               19
CreditScore          22
Balance            3636
EstimatedSalary      19
dtype: int64

df6 = dfInv1.dropna(CustomerId = 'null')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3768/1289050864.py in <module>
----> 1 df6 = dfInv1.dropna(CustomerId = 'null')

C:\ProgramData\Anaconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
    309                     stacklevel=stacklevel,
    310                 )
--> 311             return func(*args, **kwargs)
    312 
    313         return wrapper

TypeError: dropna() got an unexpected keyword argument 'CustomerId'

df5 = dfInv.dropna(['CustomerId'],axis = 1)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3768/124880380.py in <module>
----> 1 df5 = dfInv.dropna(['CustomerId'],axis = 1)

NameError: name 'dfInv' is not defined

df5= dfInv1["CreditScore"].fillna(method ='ffill', inplace = True)

dfInv1.isnull().sum()

RowNumber             0
CustomerId           19
Tenure               19
CreditScore           0
Balance            3636
EstimatedSalary      19
dtype: int64

df5= dfInv1["Balance"].fillna(method ='ffill', inplace = True)

df5= dfInv1["EstimatedSalary"].fillna(method ='ffill', inplace = True)

df5= dfInv1["Tenure"].fillna(method ='ffill', inplace = True)

df

	RowNumber	CustomerId	Exited
0	1	15634602.0	1.0
1	2	15647311.0	0.0
2	3	15619304.0	1.0
3	4	15701354.0	0.0
4	5	15737888.0	0.0
...	...	...	...
9995	9996	15606229.0	0.0
9996	9997	15569892.0	0.0
9997	9998	15584532.0	1.0
9998	9999	15682355.0	1.0
9999	10000	15628319.0	0.0

10000 rows × 3 columns

dfInv1.describe

<bound method NDFrame.describe of        RowNumber  CustomerId  Tenure  CreditScore    Balance  EstimatedSalary
0              1  15634602.0     2.0        619.0        NaN        101348.88
1              2  15647311.0     1.0        608.0   83807.86        112542.58
2              3  15619304.0     8.0        502.0  159660.80        113931.57
3              4  15701354.0     1.0        699.0  159660.80         93826.63
4              5  15737888.0     2.0        850.0  125510.82         79084.10
...          ...         ...     ...          ...        ...              ...
10014      10015         NaN     4.0        792.0  130142.79         38190.78
10015      10016         NaN     4.0        792.0  130142.79         38190.78
10016      10017         NaN     4.0        792.0  130142.79         38190.78
10017      10018         NaN     4.0        792.0  130142.79         38190.78
10018      10019         NaN     4.0        792.0  130142.79         38190.78

[10019 rows x 6 columns]>

dfInv1.describe

<bound method NDFrame.describe of        RowNumber  CustomerId  Tenure  CreditScore    Balance  EstimatedSalary
0              1  15634602.0     2.0        619.0        NaN        101348.88
1              2  15647311.0     1.0        608.0   83807.86        112542.58
2              3  15619304.0     8.0        502.0  159660.80        113931.57
3              4  15701354.0     1.0        699.0  159660.80         93826.63
4              5  15737888.0     2.0        850.0  125510.82         79084.10
...          ...         ...     ...          ...        ...              ...
10014      10015         NaN     4.0        792.0  130142.79         38190.78
10015      10016         NaN     4.0        792.0  130142.79         38190.78
10016      10017         NaN     4.0        792.0  130142.79         38190.78
10017      10018         NaN     4.0        792.0  130142.79         38190.78
10018      10019         NaN     4.0        792.0  130142.79         38190.78

[10019 rows x 6 columns]>

dfInv1.isnull().sum()

RowNumber           0
CustomerId         19
Tenure              0
CreditScore         0
Balance             1
EstimatedSalary     0
dtype: int64

df6 = dfInv1.dropna(axis=0)   
print(df6)

      RowNumber  CustomerId  Tenure  CreditScore    Balance  EstimatedSalary
1             2  15647311.0     1.0        608.0   83807.86        112542.58
2             3  15619304.0     8.0        502.0  159660.80        113931.57
3             4  15701354.0     1.0        699.0  159660.80         93826.63
4             5  15737888.0     2.0        850.0  125510.82         79084.10
5             6  15574012.0     8.0        645.0  113755.78        149756.71
...         ...         ...     ...          ...        ...              ...
9995       9996  15606229.0     5.0        771.0  155060.41         96270.64
9996       9997  15569892.0    10.0        516.0   57369.61        101699.77
9997       9998  15584532.0     7.0        709.0   57369.61         42085.58
9998       9999  15682355.0     3.0        772.0   75075.31         92888.52
9999      10000  15628319.0     4.0        792.0  130142.79         38190.78

[9999 rows x 6 columns]

df6.isnull().sum()

RowNumber          0
CustomerId         0
Tenure             0
CreditScore        0
Balance            0
EstimatedSalary    0
dtype: int64

df7 =pd.merge(df4,df6,on='CustomerId',how='inner')

df7

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	RowNumber	Tenure	CreditScore	Balance	EstimatedSalary
0	2	15647311.0	0.0	Hill	Central	Female	41.0	2	1.0	608.0	83807.86	112542.58
1	3	15619304.0	1.0	Onio	West	Female	42.0	3	8.0	502.0	159660.80	113931.57
2	4	15701354.0	0.0	Boni	West	Female	39.0	4	1.0	699.0	159660.80	93826.63
3	5	15737888.0	0.0	Mitchell	Central	Female	43.0	5	2.0	850.0	125510.82	79084.10
4	6	15574012.0	1.0	Chu	Central	Male	44.0	6	8.0	645.0	113755.78	149756.71
...	...	...	...	...	...	...	...	...	...	...	...	...
9994	9996	15606229.0	0.0	Obijiaku	West	Male	39.0	9996	5.0	771.0	155060.41	96270.64
9995	9997	15569892.0	0.0	Johnstone	West	Male	35.0	9997	10.0	516.0	57369.61	101699.77
9996	9998	15584532.0	1.0	Liu	West	Female	36.0	9998	7.0	709.0	57369.61	42085.58
9997	9999	15682355.0	1.0	Sabbatini	East	Male	42.0	9999	3.0	772.0	75075.31	92888.52
9998	10000	15628319.0	0.0	Walker	West	Female	28.0	10000	4.0	792.0	130142.79	38190.78

9999 rows × 12 columns

df8= df7.drop(['RowNumber'],axis = 1)

df8

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary
0	2	15647311.0	0.0	Hill	Central	Female	41.0	1.0	608.0	83807.86	112542.58
1	3	15619304.0	1.0	Onio	West	Female	42.0	8.0	502.0	159660.80	113931.57
2	4	15701354.0	0.0	Boni	West	Female	39.0	1.0	699.0	159660.80	93826.63
3	5	15737888.0	0.0	Mitchell	Central	Female	43.0	2.0	850.0	125510.82	79084.10
4	6	15574012.0	1.0	Chu	Central	Male	44.0	8.0	645.0	113755.78	149756.71
...	...	...	...	...	...	...	...	...	...	...	...
9994	9996	15606229.0	0.0	Obijiaku	West	Male	39.0	5.0	771.0	155060.41	96270.64
9995	9997	15569892.0	0.0	Johnstone	West	Male	35.0	10.0	516.0	57369.61	101699.77
9996	9998	15584532.0	1.0	Liu	West	Female	36.0	7.0	709.0	57369.61	42085.58
9997	9999	15682355.0	1.0	Sabbatini	East	Male	42.0	3.0	772.0	75075.31	92888.52
9998	10000	15628319.0	0.0	Walker	West	Female	28.0	4.0	792.0	130142.79	38190.78

9999 rows × 11 columns

df8.isnull().sum()

RowNumber_x        0
CustomerId         0
Exited             0
Surname            0
Geography          0
Gender             0
Age                0
Tenure             0
CreditScore        0
Balance            0
EstimatedSalary    0
dtype: int64

df8.describe()

	RowNumber_x	CustomerId	Exited	Age	Tenure	CreditScore	Balance	EstimatedSalary
count	9999.000000	9.999000e+03	9999.00000	9999.000000	9999.000000	9999.000000	9999.000000	9999.000000
mean	5001.000000	1.569095e+07	0.20362	38.943194	5.015702	650.466147	119567.688658	100090.114004
std	2886.607005	7.193758e+04	0.40271	10.614212	2.902919	96.724029	30123.499324	57513.367468
min	2.000000	1.556570e+07	0.00000	18.000000	0.000000	305.000000	3768.690000	11.580000
25%	2501.500000	1.562853e+07	0.00000	32.000000	3.000000	584.000000	99812.160000	50992.930000
50%	5001.000000	1.569074e+07	0.00000	37.000000	5.000000	652.000000	119624.540000	100187.430000
75%	7500.500000	1.575324e+07	0.00000	44.000000	7.500000	717.500000	138964.915000	149392.065000
max	10000.000000	1.581569e+07	1.00000	190.000000	30.000000	865.000000	250898.090000	199992.480000

df8.head()

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary
0	2	15647311.0	0.0	Hill	Central	Female	41.0	1.0	608.0	83807.86	112542.58
1	3	15619304.0	1.0	Onio	West	Female	42.0	8.0	502.0	159660.80	113931.57
2	4	15701354.0	0.0	Boni	West	Female	39.0	1.0	699.0	159660.80	93826.63
3	5	15737888.0	0.0	Mitchell	Central	Female	43.0	2.0	850.0	125510.82	79084.10
4	6	15574012.0	1.0	Chu	Central	Male	44.0	8.0	645.0	113755.78	149756.71

df8.tail()

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary
9994	9996	15606229.0	0.0	Obijiaku	West	Male	39.0	5.0	771.0	155060.41	96270.64
9995	9997	15569892.0	0.0	Johnstone	West	Male	35.0	10.0	516.0	57369.61	101699.77
9996	9998	15584532.0	1.0	Liu	West	Female	36.0	7.0	709.0	57369.61	42085.58
9997	9999	15682355.0	1.0	Sabbatini	East	Male	42.0	3.0	772.0	75075.31	92888.52
9998	10000	15628319.0	0.0	Walker	West	Female	28.0	4.0	792.0	130142.79	38190.78

dfPort =pd.read_csv('G:\ANTWALK\Python Project\Python Project Solution\Given_Dataset\Customer Portfolio Snapshot.csv')
dfPort

	RowNumber	CustomerId	NumOfProducts	HasChckng	IsActiveMember
0	1288	15565701	1.0	0.0	0.0
1	4199	15565706	1.0	1.0	1.0
2	7091	15565714	2.0	0.0	1.0
3	2021	15565779	1.0	1.0	0.0
4	3698	15565796	1.0	1.0	0.0
...	...	...	...	...	...
10016	10015	15815705	NaN	NaN	NaN
10017	10016	15815706	NaN	NaN	NaN
10018	10017	15815707	NaN	NaN	NaN
10019	10018	15815708	NaN	NaN	NaN
10020	10019	15815709	NaN	NaN	NaN

10021 rows × 5 columns

dfPort.isnull().sum()

RowNumber          0
CustomerId         0
NumOfProducts     19
HasChckng         19
IsActiveMember    19
dtype: int64

df9= dfPort["NumOfProducts"].fillna(method ='ffill', inplace = True)

dfPort.isnull().sum()

RowNumber          0
CustomerId         0
NumOfProducts      0
HasChckng         19
IsActiveMember    19
dtype: int64

df9= dfPort["HasChckng"].fillna(method ='ffill', inplace = True)

df9= dfPort["IsActiveMember"].fillna(method ='ffill', inplace = True)

df10 =pd.merge(df8,dfPort,on='CustomerId',how='inner')

df10.isnull().sum()

RowNumber_x        0
CustomerId         0
Exited             0
Surname            0
Geography          0
Gender             0
Age                0
Tenure             0
CreditScore        0
Balance            0
EstimatedSalary    0
RowNumber          0
NumOfProducts      0
HasChckng          0
IsActiveMember     0
dtype: int64

df10.describe

<bound method NDFrame.describe of        RowNumber_x  CustomerId  Exited    Surname Geography  Gender   Age  \
0                2  15647311.0     0.0       Hill   Central  Female  41.0   
1                3  15619304.0     1.0       Onio      West  Female  42.0   
2                4  15701354.0     0.0       Boni      West  Female  39.0   
3                5  15737888.0     0.0   Mitchell   Central  Female  43.0   
4                6  15574012.0     1.0        Chu   Central    Male  44.0   
...            ...         ...     ...        ...       ...     ...   ...   
9996          9996  15606229.0     0.0   Obijiaku      West    Male  39.0   
9997          9997  15569892.0     0.0  Johnstone      West    Male  35.0   
9998          9998  15584532.0     1.0        Liu      West  Female  36.0   
9999          9999  15682355.0     1.0  Sabbatini      East    Male  42.0   
10000        10000  15628319.0     0.0     Walker      West  Female  28.0   

       Tenure  CreditScore    Balance  EstimatedSalary  RowNumber  \
0         1.0        608.0   83807.86        112542.58          2   
1         8.0        502.0  159660.80        113931.57          3   
2         1.0        699.0  159660.80         93826.63          4   
3         2.0        850.0  125510.82         79084.10          5   
4         8.0        645.0  113755.78        149756.71          6   
...       ...          ...        ...              ...        ...   
9996      5.0        771.0  155060.41         96270.64       9996   
9997     10.0        516.0   57369.61        101699.77       9997   
9998      7.0        709.0   57369.61         42085.58       9998   
9999      3.0        772.0   75075.31         92888.52       9999   
10000     4.0        792.0  130142.79         38190.78      10000   

       NumOfProducts  HasChckng  IsActiveMember  
0                1.0        0.0             1.0  
1                3.0        1.0             0.0  
2                2.0        0.0             0.0  
3                1.0        1.0             1.0  
4                2.0        1.0             0.0  
...              ...        ...             ...  
9996             2.0        1.0             0.0  
9997             1.0        1.0             1.0  
9998             1.0        0.0             1.0  
9999             2.0        1.0             0.0  
10000            1.0        1.0             0.0  

[10001 rows x 15 columns]>

df.tail()

	RowNumber	CustomerId	Exited
9995	9996	15606229.0	0.0
9996	9997	15569892.0	0.0
9997	9998	15584532.0	1.0
9998	9999	15682355.0	1.0
9999	10000	15628319.0	0.0

df.head()

	RowNumber	CustomerId	Exited
0	1	15634602.0	1.0
1	2	15647311.0	0.0
2	3	15619304.0	1.0
3	4	15701354.0	0.0
4	5	15737888.0	0.0

df10

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary	RowNumber	NumOfProducts	HasChckng	IsActiveMember
0	2	15647311.0	0.0	Hill	Central	Female	41.0	1.0	608.0	83807.86	112542.58	2	1.0	0.0	1.0
1	3	15619304.0	1.0	Onio	West	Female	42.0	8.0	502.0	159660.80	113931.57	3	3.0	1.0	0.0
2	4	15701354.0	0.0	Boni	West	Female	39.0	1.0	699.0	159660.80	93826.63	4	2.0	0.0	0.0
3	5	15737888.0	0.0	Mitchell	Central	Female	43.0	2.0	850.0	125510.82	79084.10	5	1.0	1.0	1.0
4	6	15574012.0	1.0	Chu	Central	Male	44.0	8.0	645.0	113755.78	149756.71	6	2.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9996	9996	15606229.0	0.0	Obijiaku	West	Male	39.0	5.0	771.0	155060.41	96270.64	9996	2.0	1.0	0.0
9997	9997	15569892.0	0.0	Johnstone	West	Male	35.0	10.0	516.0	57369.61	101699.77	9997	1.0	1.0	1.0
9998	9998	15584532.0	1.0	Liu	West	Female	36.0	7.0	709.0	57369.61	42085.58	9998	1.0	0.0	1.0
9999	9999	15682355.0	1.0	Sabbatini	East	Male	42.0	3.0	772.0	75075.31	92888.52	9999	2.0	1.0	0.0
10000	10000	15628319.0	0.0	Walker	West	Female	28.0	4.0	792.0	130142.79	38190.78	10000	1.0	1.0	0.0

10001 rows × 15 columns

df11= df10.drop(['RowNumber'],axis = 1)

df11

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary	NumOfProducts	HasChckng	IsActiveMember
0	2	15647311.0	0.0	Hill	Central	Female	41.0	1.0	608.0	83807.86	112542.58	1.0	0.0	1.0
1	3	15619304.0	1.0	Onio	West	Female	42.0	8.0	502.0	159660.80	113931.57	3.0	1.0	0.0
2	4	15701354.0	0.0	Boni	West	Female	39.0	1.0	699.0	159660.80	93826.63	2.0	0.0	0.0
3	5	15737888.0	0.0	Mitchell	Central	Female	43.0	2.0	850.0	125510.82	79084.10	1.0	1.0	1.0
4	6	15574012.0	1.0	Chu	Central	Male	44.0	8.0	645.0	113755.78	149756.71	2.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9996	9996	15606229.0	0.0	Obijiaku	West	Male	39.0	5.0	771.0	155060.41	96270.64	2.0	1.0	0.0
9997	9997	15569892.0	0.0	Johnstone	West	Male	35.0	10.0	516.0	57369.61	101699.77	1.0	1.0	1.0
9998	9998	15584532.0	1.0	Liu	West	Female	36.0	7.0	709.0	57369.61	42085.58	1.0	0.0	1.0
9999	9999	15682355.0	1.0	Sabbatini	East	Male	42.0	3.0	772.0	75075.31	92888.52	2.0	1.0	0.0
10000	10000	15628319.0	0.0	Walker	West	Female	28.0	4.0	792.0	130142.79	38190.78	1.0	1.0	0.0

10001 rows × 14 columns

df12 = df11(['CustomerId'].astype(int))

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3768/1581286670.py in <module>
----> 1 df12 = df11(['CustomerId'].astype(int))

AttributeError: 'list' object has no attribute 'astype'

df12

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3768/2166137298.py in <module>
----> 1 df12

NameError: name 'df12' is not defined

df13 = df11.astype({"CustomerId":'int', "EstimatedSalary":'int',"Age":'int',"Tenure":'int',"CreditScore":'int',"Exited":'int'})

df14 = df13.astype({"HasChckng":'int'})

df14

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary	NumOfProducts	HasChckng	IsActiveMember
0	2	15647311	0	Hill	Central	Female	41	1	608	83807.86	112542	1.0	0	1.0
1	3	15619304	1	Onio	West	Female	42	8	502	159660.80	113931	3.0	1	0.0
2	4	15701354	0	Boni	West	Female	39	1	699	159660.80	93826	2.0	0	0.0
3	5	15737888	0	Mitchell	Central	Female	43	2	850	125510.82	79084	1.0	1	1.0
4	6	15574012	1	Chu	Central	Male	44	8	645	113755.78	149756	2.0	1	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9996	9996	15606229	0	Obijiaku	West	Male	39	5	771	155060.41	96270	2.0	1	0.0
9997	9997	15569892	0	Johnstone	West	Male	35	10	516	57369.61	101699	1.0	1	1.0
9998	9998	15584532	1	Liu	West	Female	36	7	709	57369.61	42085	1.0	0	1.0
9999	9999	15682355	1	Sabbatini	East	Male	42	3	772	75075.31	92888	2.0	1	0.0
10000	10000	15628319	0	Walker	West	Female	28	4	792	130142.79	38190	1.0	1	0.0

10001 rows × 14 columns

display.df13.dtypes()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3768/1693962844.py in <module>
----> 1 display.df13.dtypes()

AttributeError: 'function' object has no attribute 'df13'

display(df14.dtypes)

RowNumber_x          int64
CustomerId           int32
Exited               int32
Surname             object
Geography           object
Gender              object
Age                  int32
Tenure               int32
CreditScore          int32
Balance            float64
EstimatedSalary      int32
NumOfProducts      float64
HasChckng            int32
IsActiveMember     float64
dtype: object

df15 = df14.astype({"EstimatedSalary":'float'})
df15

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary	NumOfProducts	HasChckng	IsActiveMember
0	2	15647311	0	Hill	Central	Female	41	1	608	83807.86	112542.0	1.0	0	1.0
1	3	15619304	1	Onio	West	Female	42	8	502	159660.80	113931.0	3.0	1	0.0
2	4	15701354	0	Boni	West	Female	39	1	699	159660.80	93826.0	2.0	0	0.0
3	5	15737888	0	Mitchell	Central	Female	43	2	850	125510.82	79084.0	1.0	1	1.0
4	6	15574012	1	Chu	Central	Male	44	8	645	113755.78	149756.0	2.0	1	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9996	9996	15606229	0	Obijiaku	West	Male	39	5	771	155060.41	96270.0	2.0	1	0.0
9997	9997	15569892	0	Johnstone	West	Male	35	10	516	57369.61	101699.0	1.0	1	1.0
9998	9998	15584532	1	Liu	West	Female	36	7	709	57369.61	42085.0	1.0	0	1.0
9999	9999	15682355	1	Sabbatini	East	Male	42	3	772	75075.31	92888.0	2.0	1	0.0
10000	10000	15628319	0	Walker	West	Female	28	4	792	130142.79	38190.0	1.0	1	0.0

10001 rows × 14 columns

display(df15.dtypes)

RowNumber_x          int64
CustomerId           int32
Exited               int32
Surname             object
Geography           object
Gender              object
Age                  int32
Tenure               int32
CreditScore          int32
Balance            float64
EstimatedSalary    float64
NumOfProducts      float64
HasChckng            int32
IsActiveMember     float64
dtype: object

df16 = df14.astype({"IsActiveMember":'int'})

display(df16.dtypes)

RowNumber_x          int64
CustomerId           int32
Exited               int32
Surname             object
Geography           object
Gender              object
Age                  int32
Tenure               int32
CreditScore          int32
Balance            float64
EstimatedSalary      int32
NumOfProducts      float64
HasChckng            int32
IsActiveMember       int32
dtype: object

df17 = df14.astype({"NumOfProducts":'int',"EstimatedSalary":'float' })

df17

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary	NumOfProducts	HasChckng	IsActiveMember
0	2	15647311	0	Hill	Central	Female	41	1	608	83807.86	112542.0	1	0	1.0
1	3	15619304	1	Onio	West	Female	42	8	502	159660.80	113931.0	3	1	0.0
2	4	15701354	0	Boni	West	Female	39	1	699	159660.80	93826.0	2	0	0.0
3	5	15737888	0	Mitchell	Central	Female	43	2	850	125510.82	79084.0	1	1	1.0
4	6	15574012	1	Chu	Central	Male	44	8	645	113755.78	149756.0	2	1	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9996	9996	15606229	0	Obijiaku	West	Male	39	5	771	155060.41	96270.0	2	1	0.0
9997	9997	15569892	0	Johnstone	West	Male	35	10	516	57369.61	101699.0	1	1	1.0
9998	9998	15584532	1	Liu	West	Female	36	7	709	57369.61	42085.0	1	0	1.0
9999	9999	15682355	1	Sabbatini	East	Male	42	3	772	75075.31	92888.0	2	1	0.0
10000	10000	15628319	0	Walker	West	Female	28	4	792	130142.79	38190.0	1	1	0.0

10001 rows × 14 columns

import seaborn as sns

df17.head()

	RowNumber_x	CustomerId	Exited	Surname	Geography	Gender	Age	Tenure	CreditScore	Balance	EstimatedSalary	NumOfProducts	HasChckng	IsActiveMember
0	2	15647311	0	Hill	Central	Female	41	1	608	83807.86	112542.0	1	0	1.0
1	3	15619304	1	Onio	West	Female	42	8	502	159660.80	113931.0	3	1	0.0
2	4	15701354	0	Boni	West	Female	39	1	699	159660.80	93826.0	2	0	0.0
3	5	15737888	0	Mitchell	Central	Female	43	2	850	125510.82	79084.0	1	1	1.0
4	6	15574012	1	Chu	Central	Male	44	8	645	113755.78	149756.0	2	1	0.0

df17.rename({("RowNumber_x","RowNumber")})

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3768/4111734053.py in <module>
----> 1 df17.rename({("RowNumber_x","RowNumber")})

C:\ProgramData\Anaconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
    322         @wraps(func)
    323         def wrapper(*args, **kwargs) -> Callable[..., Any]:
--> 324             return func(*args, **kwargs)
    325 
    326         kind = inspect.Parameter.POSITIONAL_OR_KEYWORD

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5037         4  3  6
   5038         """
-> 5039         return super().rename(
   5040             mapper=mapper,
   5041             index=index,

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1154                     raise KeyError(f"{missing_labels} not found in axis")
   1155 
-> 1156             new_index = ax._transform_index(f, level)
   1157             result._set_axis_nocheck(new_index, axis=axis_no, inplace=True)
   1158             result._clear_item_cache()

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in _transform_index(self, func, level)
   5544             return type(self).from_tuples(items, names=self.names)
   5545         else:
-> 5546             items = [func(x) for x in self]
   5547             return Index(items, name=self.name, tupleize_cols=False)
   5548 

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in <listcomp>(.0)
   5544             return type(self).from_tuples(items, names=self.names)
   5545         else:
-> 5546             items = [func(x) for x in self]
   5547             return Index(items, name=self.name, tupleize_cols=False)
   5548 

TypeError: 'set' object is not callable

df17.corr()

	RowNumber_x	CustomerId	Exited	Age	Tenure	CreditScore	Balance	EstimatedSalary	NumOfProducts	HasChckng	IsActiveMember
RowNumber_x	1.000000	0.003785	-0.016548	-0.000407	-0.008031	0.006779	0.001511	-0.006201	0.006675	0.000962	0.012380
CustomerId	0.003785	1.000000	-0.005406	0.008682	-0.015697	0.005952	-0.004549	0.015729	0.017760	-0.014503	0.001384
Exited	-0.016548	-0.005406	1.000000	0.280832	-0.012380	-0.027459	0.012649	0.012617	-0.046604	-0.007874	-0.156688
Age	-0.000407	0.008682	0.280832	1.000000	-0.009015	-0.004014	-0.016728	-0.009153	-0.028571	-0.013325	0.082298
Tenure	-0.008031	-0.015697	-0.012380	-0.009015	1.000000	-0.002303	0.008880	0.007777	0.016567	0.023460	-0.028862
CreditScore	0.006779	0.005952	-0.027459	-0.004014	-0.002303	1.000000	-0.002624	-0.002456	0.012346	-0.005914	0.025734
Balance	0.001511	-0.004549	0.012649	-0.016728	0.008880	-0.002624	1.000000	0.000653	-0.009113	0.012899	-0.012080
EstimatedSalary	-0.006201	0.015729	0.012617	-0.009153	0.007777	-0.002456	0.000653	1.000000	0.014871	-0.010345	-0.011696
NumOfProducts	0.006675	0.017760	-0.046604	-0.028571	0.016567	0.012346	-0.009113	0.014871	1.000000	0.002458	0.009174
HasChckng	0.000962	-0.014503	-0.007874	-0.013325	0.023460	-0.005914	0.012899	-0.010345	0.002458	1.000000	-0.011606
IsActiveMember	0.012380	0.001384	-0.156688	0.082298	-0.028862	0.025734	-0.012080	-0.011696	0.009174	-0.011606	1.000000

sns.heatmap(df17.corr())

<AxesSubplot:>

sns.jointplot(x='Balance',y='NumOfProducts',data=df17,kind='hex')

<seaborn.axisgrid.JointGrid at 0x2e1361fda30>

sns.jointplot(x='Balance',y='NumOfProducts',data=df17,kind='reg')

<seaborn.axisgrid.JointGrid at 0x2e13a939430>

sns.jointplot(x='IsActiveMember',y='Exited',data=df17,kind='reg')

<seaborn.axisgrid.JointGrid at 0x2e13ae0c580>

sns.pairplot(df17)

<seaborn.axisgrid.PairGrid at 0x2e13c8e9730>

sns.pairplot(df17,hue='Exited')

<seaborn.axisgrid.PairGrid at 0x2e1504b2640>

sns.distplot(df17['Exited'],kde=False,)

<AxesSubplot:xlabel='Exited'>

sns.distplot(df17['IsActiveMember'],kde=False)

<AxesSubplot:xlabel='IsActiveMember'>

sns.countplot('IsActiveMember',data=df17)

<AxesSubplot:xlabel='IsActiveMember', ylabel='count'>

sns.countplot('Exited',data=df17)

<AxesSubplot:xlabel='Exited', ylabel='count'>

sns.countplot('CreditScore',data=df17)

<AxesSubplot:xlabel='CreditScore', ylabel='count'>

sns.countplot('Tenure',data=df17)

<AxesSubplot:xlabel='Tenure', ylabel='count'>

sns.countplot(y='IsActiveMember',data=df17)

<AxesSubplot:xlabel='count', ylabel='IsActiveMember'>

sns.boxplot(x='Exited',y='IsActiveMember',data=df17)

<AxesSubplot:xlabel='Exited', ylabel='IsActiveMember'>

sns.boxplot(y='Age',x='IsActiveMember',data=df17)

<AxesSubplot:xlabel='IsActiveMember', ylabel='Age'>

sns.boxplot(y='Age',x='NumOfProducts',data=df17)

<AxesSubplot:xlabel='NumOfProducts', ylabel='Age'>

sns.boxplot(y='EstimatedSalary',x='NumOfProducts',data=df17)

<AxesSubplot:xlabel='NumOfProducts', ylabel='EstimatedSalary'>

sns.violinplot(y='EstimatedSalary',x='NumOfProducts',data=df17)

<AxesSubplot:xlabel='NumOfProducts', ylabel='EstimatedSalary'>